Learning accurate drug representations is essential for task such as computational drug repositioning. A drug hierarchy is a valuable source that encodes knowledge of relations among drugs in a tree-like structure where drugs that act on the same organs, treat the same disease, or bind to the same biological target are grouped together. However, its utility in learning drug representations has not yet been explored, and currently described drug representations cannot place novel molecules in a drug hierarchy. Here, we develop a semi-supervised drug embedding that incorporates two sources of information: (1) underlying chemical grammar that is inferred from chemical structures of drugs and drug-like molecules (unsupervised), and (2) hierarchical relations that are encoded in an expert-crafted hierarchy of approved drugs (supervised). We use the Variational Auto-Encoder (VAE) framework to encode the chemical structures of molecules and use the drug-drug similarity information obtained from the hierarchy to induce the clustering of drugs in hyperbolic space. The hyperbolic space is amenable for encoding hierarchical relations. Our qualitative results support that the learned drug embedding can induce the hierarchical relations among drugs. We demonstrate that the learned drug embedding can be used for drug repositioning.

The A.I. disruption and the need to compete on innovation are impacting cities that have an increasing necessity to become innovation hotspots. However, without proven solutions, experimentation, often unsuccessful, is needed. But experimentation in cities has many undesirable effects not only for its citizens but also reputational if unsuccessful. Digital Twins, so popular in other areas, seem like a promising way to expand experimentation proposals but in simulated environments, translating only the half-baked ones, the ones with higher probability of success, to real environments and therefore minimizing risks. However, Digital Twins are data intensive and need highly localized data, making them difficult to scale, particularly to small cities, and with the high cost associated to data collection. We present an alternative based on synthetic data that given some conditions, quite common in Smart Cities, can solve these two problems together with a proof-of-concept based on NO2 pollution.

In this paper, we proposed a new clustering-based active learning framework, namely Active Learning using a Clustering-based Sampling (ALCS), to address the shortage of labeled data. ALCS employs a density-based clustering approach to explore the cluster structure from the data without requiring exhaustive parameter tuning. A bi-cluster boundary-based sample query procedure is introduced to improve the learning performance for classifying highly overlapped classes. Additionally, we developed an effective diversity exploration strategy to address the redundancy among queried samples.

While most millionaires spend their fortune on pricey cars and luxurious boats, William Sachiti used his fortune to purchase a Cold War era radar station in Norwich, England. Sachiti, who is a British entrepreneur, purchased a network of private roads along a 250,000 square mile to test his'spaceship, alien-looking' autonomous vehicles, but the radar system was an added bonus. 'UFOs obviously,' he jokingly told DailyMail.com in response to being asked what he plans on doing with the gigantic 25-foot-tall machine that once alerted the British army to oncoming nuclear missiles. 'I will find a way to bring this to life and let the people choose the best way to use it,' he said. 'If people want to hunt UFOS, I guess it is hunting UFOs.'

Artificial intelligence (AI) can devise methods of wealth distribution that are more popular than systems designed by people, new research suggests. The findings, made by a team of researchers at UK-based AI company DeepMind, show that machine learning systems aren't just good at solving complex physics and biology problems, but may also help deliver on more open-ended social objectives, such as the goal of realizing a fair, prosperous society. Building a machine that can deliver beneficial results humans actually want – called "value alignment" in AI research – is complicated by the fact that people often disagree on the best method to resolve all kinds of things, and especially social, economic, and political issues. "One key hurdle for value alignment is that human society admits a plurality of views, making it unclear to whose preferences AI should align," researchers explain in a new paper, led by first author and DeepMind research scientist Raphael Koster. "For example, political scientists and economists are often at loggerheads over which mechanisms will make our societies function most fairly or efficiently."

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders. NVIDIA is a multinational company known for its computing hardware, especially its graphics processing units (GPUs) and systems on chip units (SoCs) for mobile devices. The company went public on January 22, 1999. While the company remains focused on hardware production, it has implemented deep learning and AI into its GPUs and specific software, such as its autonomous driving platform. The company trades on the NASDAQ (symbol: NVDA) with a market cap of just above $418 billion and employs approximately 23,000 globally.

War is the worst human invention that ruins lives and leaves unhealed wounds for many generations. The wars of the past Millennium had at least some honesty in them in the way that they were declared, and it was clear who was against whom. However, modern-day warfare is different. The informational war is a huge portion of the ongoing war between Ukraine and Russia, with a lot of fake news that manipulates public opinion. The government of Russia has deployed entire networks of TV channels that target Western audiences, and now they are heavily utilizing them to change the world's opinion about Ukraine, make false claims about it, and convince that Russia is not killing people but "saving them."

South London and Maudsley NHS Foundation Trust is working with Virgin Media O2 to switch on the UK's first 5G-connected hospital. The switch-on is part of Maudsley Digital Lab's series of digital health and innovation trials funded by NHS Digital. The trials are investigating the efficiency, safety and security benefits of using smart, 5G-connected technologies in NHS hospitals – including IoT (Internet of Things), AR (Augmented Reality) and AI (Artificial Intelligence). Trials are now live across two wards at Bethlem Royal Hospital in South London. These include dedicated, near-real-time connectivity to power e-Observations, where clinicians use handheld devices to update patient records.

Federal officials have largely come around to the idea that research funding is crucial for U.S. leadership in artificial intelligence, but there are ways to accelerate innovation besides pouring in more money, according to tech experts. For one, they said, the government could map a long-term strategy for advancing the technology. "The U.S. has been slow in making this a national imperative," Dean Garfield, president and CEO of the Information Technology Industry Council, said Thursday on a panel hosted by Politico. "The signal that comes from the top … is critically important here and has the opportunity to really catalyze that action in a way that wouldn't happen without it." The Office of Science and Technology Policy on Wednesday requested industry input on updating an AI research and development strategy the White House published in 2016.

FIFA, the international governing body of association football,* has announced it will use AI-powered cameras to help referees make offside calls at the 2022 World Cup. The semi-automated system consists of a sensor in the ball that relays its position on the field 500 times a second, and 12 tracking cameras mounted underneath the roof of stadiums, which use machine learning to track 29 points in players' bodies. Software will combine this data to generate automated alerts when players commit offside offenses (that is: when they're nearer to the other team's goal than their second-last opponent and receiving the ball). Alerts will be sent to officials in a nearby control room, who will validate the decision and tell referees on the field what call to make. FIFA claims this process will happen "within a few seconds and means that offside decisions can be made faster and more accurately."